What do Oscar-winning movies, the perfect Christmas card,
and a great analytic strategy all have in common? They're all byproducts of
great editing. Editors are often unsung heroes of masterpiece productions, and
your big data analytic strategy is no different. In my college days as an
Organizational Leadership major, I wrote a lot of thesis papers. This is where
I learned the value of having a great editor. My college had a free editing service where English
professors would offer suggestions on how to improve your paper. I used them
for every single paper, and in every case, my paper was clearer, more concise,
and more collegiate. I've carried this best practice into my professional life,
and it has served me well. With all the authoring I do, I never publish an
important document without having an editor review it. Surprisingly, I don't
often see editors on data science strategy teams - this is a big mistake. To
make the most effective use of your data science team, employ the skills of a
professional editor.

Editors for leadership and management

Just like you have experts in leadership, management, and of
course analytics on your data science team; you must have experts in
communication. Editors articulate thoughts and ideas into effective
communication media, and there's no strategy or data science team that doesn't
need this. So, without a specific role to play this part, editing often becomes
a group exercise among your most expensive and least appropriate resources.
I've often seen managers unofficially anointed to this thankless role, and even
if they are good, wouldn't you rather have them managing the team? Worse yet,
the manager's draft now goes up to the leaders who must now spend their time in
edits instead of setting the course. This is a gross misuse of valuable
resources. With relatively little additional expense, hire an editor to worry
about content - they're the experts.

Perhaps the most obvious and direct application of editors
is in the leadership function. Leaders are responsible for articulating the
vision and helping the organization through the anxiety of unchartered waters.
Having a big data analytic component to your corporate strategy makes this task
even more daunting. Although many are coming around to the idea of embracing
analytics in a corporate strategy, it's still a black box to the masses. A good
editor can help explain unfamiliar concepts like machine learning and natural
language processing in a way that makes the rest of the organization relate
them to the company's future.

Although leaders obviously benefit from having an editor
around, management could really use a hand as well. Managers are busy, busy
people - busier than anyone else on the team. The last thing you want to do is
tax them with another responsibility; however, it seems like managers are the
de facto editors when nobody else is assigned. Communication is vitally
important to managers - it's they're primary tool for harnessing all the
complexity of a strategy, especially one that involves big data analytics. A
manager should be able to throw rough ideas and bullet points to an editor, and
in return, get clean and clear reports and presentations that can be shared
with leadership, the data science team, and the rest of the organization. The
trick here is to make sure your editor has enough talent and context to follow
what's going on. If the editor's output needs to be edited by the manager - that
defeats the purpose!

Editors and data scientists

There's a special kind of editor that can add a great deal
of value to the data science team itself. The role of this editor is to clarify
the work produced by the data science team. For instance, your data science
team may be tasked with qualitative analysis against your operational data. One
of the key outputs of this exercise is a research paper that explains their
findings. The editor in this case must ensure this paper is well written.
Although the goal for the editor is to make this type of work generally
consumable, the main audience for this editing is other data scientists.

Data science editing is not confined to research papers.
Remember, data scientists are not only writing papers, but they're also writing
code. Having a deep background in computer science, and having worked closely
with other computer programmers, I can assure you that programmers have a bad
habit of writing unreadable code. I shamefully admit that, on occasion, I've
opened up my own code after months or years, only to find a foreign language
staring back at me - even though I originally wrote it.

One method of overcoming spaghetti-code syndrome, that's
popular in the agile community, is refactoring.
Refactoring is an exercise where programmers rewrite functioning code, for
better readability and design. That sounds a lot like editing to me! Why not
have an editor (or team of editors) do nothing but refactor data science code?
That way, the data scientists can continue cranking out code without worrying
about unreadable code or slowing down to refactor.

At first glance, you would think that the only people that
could do this type of editing are other data scientists, but that's not
entirely true. Sure, you need someone with special skills; however, it's easier
to edit a book than it is to write a book. Your data science editors should
have excellent programming skills, and excellent communication skills, but
their analytic skills don't need to be superior. As long as they can understand
what the data scientists are trying to accomplish, they'll be fine. The key to
making this work is a good testing infrastructure. This prevents the disaster
of editors, with good intentions, breaking functional code. By the way, this
doesn't only apply to code. On more than one occasion, I've had editors improve
my writing, only to mess up the message. It happens. That's why you should
always have a final review with the authoring team. However, if your editor is
good, it should be a quick and painless review.

Bottom line

If you care about communication, and its role in your big data
analytic strategy - which you should - then hire communication experts to do
the job. Although you could tax your leaders, managers, and data scientists
with their own editing; it's much more efficient to have editors on board. Like
any other expert, they're not only better at producing high-quality
communication media, but they're also quicker and cheaper. Editors can help out
with leadership, management, and internal data science communication; however,
it takes special skills to work on a data science team. Make sure they know
enough to follow along, translate difficult concepts and ideas into
understandable media, and write code if necessary. Take some time to think
about who's doing all the editing on your data science team right now. If it's
your leaders, managers, or your high-powered data scientists, you might want to
edit that practice out.